Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric7
Categorical4

Alerts

EER is highly overall correlated with Efficiency ScoreHigh correlation
Efficiency Score is highly overall correlated with EERHigh correlation

Reproduction

Analysis started2025-01-26 22:13:45.091405
Analysis finished2025-01-26 22:13:53.134673
Duration8.04 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Power Consumption (kW)
Real number (ℝ)

Distinct432
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.45122
Minimum1.02
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:53.248353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile1.23
Q12.1825
median3.485
Q34.7225
95-th percentile5.74
Maximum6
Range4.98
Interquartile range (IQR)2.54

Descriptive statistics

Standard deviation1.4606486
Coefficient of variation (CV)0.42322675
Kurtosis-1.2297112
Mean3.45122
Median Absolute Deviation (MAD)1.275
Skewness0.038864962
Sum3451.22
Variance2.1334944
MonotonicityNot monotonic
2025-01-26T18:13:53.423745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 8
 
0.8%
5.75 7
 
0.7%
1.23 7
 
0.7%
5.48 6
 
0.6%
1.91 6
 
0.6%
5.93 6
 
0.6%
1.13 6
 
0.6%
5.85 6
 
0.6%
5.81 5
 
0.5%
3.51 5
 
0.5%
Other values (422) 938
93.8%
ValueCountFrequency (%)
1.02 1
 
0.1%
1.03 3
0.3%
1.05 3
0.3%
1.06 2
0.2%
1.07 4
0.4%
1.08 2
0.2%
1.09 3
0.3%
1.1 3
0.3%
1.11 1
 
0.1%
1.12 2
0.2%
ValueCountFrequency (%)
6 1
 
0.1%
5.98 2
 
0.2%
5.96 1
 
0.1%
5.95 3
0.3%
5.94 1
 
0.1%
5.93 6
0.6%
5.92 2
 
0.2%
5.91 1
 
0.1%
5.9 1
 
0.1%
5.88 1
 
0.1%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
30000
214 
12000
212 
24000
208 
9000
193 
18000
173 

Length

Max length5
Median length5
Mean length4.807
Min length4

Characters and Unicode

Total characters4807
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24000
2nd row18000
3rd row30000
4th row9000
5th row30000

Common Values

ValueCountFrequency (%)
30000 214
21.4%
12000 212
21.2%
24000 208
20.8%
9000 193
19.3%
18000 173
17.3%

Length

2025-01-26T18:13:53.569236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T18:13:53.702630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30000 214
21.4%
12000 212
21.2%
24000 208
20.8%
9000 193
19.3%
18000 173
17.3%

Most occurring characters

ValueCountFrequency (%)
0 3214
66.9%
2 420
 
8.7%
1 385
 
8.0%
3 214
 
4.5%
4 208
 
4.3%
9 193
 
4.0%
8 173
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3214
66.9%
2 420
 
8.7%
1 385
 
8.0%
3 214
 
4.5%
4 208
 
4.3%
9 193
 
4.0%
8 173
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3214
66.9%
2 420
 
8.7%
1 385
 
8.0%
3 214
 
4.5%
4 208
 
4.3%
9 193
 
4.0%
8 173
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3214
66.9%
2 420
 
8.7%
1 385
 
8.0%
3 214
 
4.5%
4 208
 
4.3%
9 193
 
4.0%
8 173
 
3.6%

Noise Level (dB)
Real number (ℝ)

Distinct820
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.60257
Minimum35
Maximum59.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:53.849448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile36.1095
Q141.04
median48.04
Q353.85
95-th percentile58.7315
Maximum59.99
Range24.99
Interquartile range (IQR)12.81

Descriptive statistics

Standard deviation7.3159862
Coefficient of variation (CV)0.15368889
Kurtosis-1.2188854
Mean47.60257
Median Absolute Deviation (MAD)6.345
Skewness-0.054082207
Sum47602.57
Variance53.523654
MonotonicityNot monotonic
2025-01-26T18:13:54.015680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.95 4
 
0.4%
43.51 3
 
0.3%
59.87 3
 
0.3%
58.27 3
 
0.3%
59.27 3
 
0.3%
38.25 3
 
0.3%
37.34 3
 
0.3%
49.43 3
 
0.3%
56.93 3
 
0.3%
59.49 3
 
0.3%
Other values (810) 969
96.9%
ValueCountFrequency (%)
35 1
0.1%
35.05 2
0.2%
35.06 1
0.1%
35.07 2
0.2%
35.08 1
0.1%
35.1 1
0.1%
35.14 1
0.1%
35.15 1
0.1%
35.17 1
0.1%
35.18 1
0.1%
ValueCountFrequency (%)
59.99 2
0.2%
59.93 1
 
0.1%
59.9 1
 
0.1%
59.87 3
0.3%
59.81 1
 
0.1%
59.79 2
0.2%
59.78 2
0.2%
59.76 2
0.2%
59.73 1
 
0.1%
59.71 2
0.2%

EER
Real number (ℝ)

High correlation 

Distinct200
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.52864
Minimum2.5
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:54.187880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.6
Q13.06
median3.54
Q34.02
95-th percentile4.39
Maximum4.5
Range2
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.56716067
Coefficient of variation (CV)0.16073067
Kurtosis-1.1291372
Mean3.52864
Median Absolute Deviation (MAD)0.48
Skewness-0.086580709
Sum3528.64
Variance0.32167122
MonotonicityNot monotonic
2025-01-26T18:13:54.638212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.07 14
 
1.4%
2.53 13
 
1.3%
4.38 12
 
1.2%
3.53 12
 
1.2%
4.05 12
 
1.2%
3.33 12
 
1.2%
2.91 11
 
1.1%
3.36 10
 
1.0%
3.76 10
 
1.0%
3.09 10
 
1.0%
Other values (190) 884
88.4%
ValueCountFrequency (%)
2.5 2
 
0.2%
2.51 6
0.6%
2.52 5
 
0.5%
2.53 13
1.3%
2.54 4
 
0.4%
2.55 2
 
0.2%
2.56 4
 
0.4%
2.57 3
 
0.3%
2.58 3
 
0.3%
2.59 7
0.7%
ValueCountFrequency (%)
4.5 1
 
0.1%
4.49 2
 
0.2%
4.48 8
0.8%
4.47 6
0.6%
4.46 5
0.5%
4.45 5
0.5%
4.44 2
 
0.2%
4.43 2
 
0.2%
4.42 4
0.4%
4.41 5
0.5%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
30
212 
25
210 
20
202 
18
196 
15
180 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row30
3rd row30
4th row18
5th row30

Common Values

ValueCountFrequency (%)
30 212
21.2%
25 210
21.0%
20 202
20.2%
18 196
19.6%
15 180
18.0%

Length

2025-01-26T18:13:54.792990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T18:13:54.914909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30 212
21.2%
25 210
21.0%
20 202
20.2%
18 196
19.6%
15 180
18.0%

Most occurring characters

ValueCountFrequency (%)
0 414
20.7%
2 412
20.6%
5 390
19.5%
1 376
18.8%
3 212
10.6%
8 196
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 414
20.7%
2 412
20.6%
5 390
19.5%
1 376
18.8%
3 212
10.6%
8 196
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 414
20.7%
2 412
20.6%
5 390
19.5%
1 376
18.8%
3 212
10.6%
8 196
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 414
20.7%
2 412
20.6%
5 390
19.5%
1 376
18.8%
3 212
10.6%
8 196
9.8%

Airflow (m³/min)
Real number (ℝ)

Distinct800
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.87974
Minimum8.04
Maximum29.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:55.061747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.04
5-th percentile9.179
Q113.5075
median18.83
Q324.42
95-th percentile28.591
Maximum29.98
Range21.94
Interquartile range (IQR)10.9125

Descriptive statistics

Standard deviation6.3019744
Coefficient of variation (CV)0.33379561
Kurtosis-1.2166343
Mean18.87974
Median Absolute Deviation (MAD)5.5
Skewness-0.0022559857
Sum18879.74
Variance39.714881
MonotonicityNot monotonic
2025-01-26T18:13:55.226625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.97 4
 
0.4%
26.05 4
 
0.4%
23.52 4
 
0.4%
19.31 4
 
0.4%
12.85 3
 
0.3%
26.4 3
 
0.3%
15.17 3
 
0.3%
25.42 3
 
0.3%
13.88 3
 
0.3%
13.83 3
 
0.3%
Other values (790) 966
96.6%
ValueCountFrequency (%)
8.04 1
0.1%
8.05 1
0.1%
8.06 1
0.1%
8.08 1
0.1%
8.11 1
0.1%
8.15 2
0.2%
8.16 2
0.2%
8.17 1
0.1%
8.19 1
0.1%
8.21 1
0.1%
ValueCountFrequency (%)
29.98 1
0.1%
29.96 1
0.1%
29.95 1
0.1%
29.93 1
0.1%
29.9 2
0.2%
29.86 1
0.1%
29.85 1
0.1%
29.84 1
0.1%
29.82 1
0.1%
29.71 1
0.1%

Warranty (Years)
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
230 
6
198 
2
196 
4
191 
5
185 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row6
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Length

2025-01-26T18:13:55.424156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T18:13:55.553978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Most occurring characters

ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 230
23.0%
6 198
19.8%
2 196
19.6%
4 191
19.1%
5 185
18.5%

Build Quality Rating
Real number (ℝ)

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.551
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:55.679047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9631367
Coefficient of variation (CV)0.53380233
Kurtosis-1.2804266
Mean5.551
Median Absolute Deviation (MAD)3
Skewness-0.026963651
Sum5551
Variance8.7801792
MonotonicityNot monotonic
2025-01-26T18:13:55.799184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 114
11.4%
1 112
11.2%
9 111
11.1%
4 103
10.3%
6 101
10.1%
2 100
10.0%
7 99
9.9%
3 89
8.9%
8 89
8.9%
5 82
8.2%
ValueCountFrequency (%)
1 112
11.2%
2 100
10.0%
3 89
8.9%
4 103
10.3%
5 82
8.2%
6 101
10.1%
7 99
9.9%
8 89
8.9%
9 111
11.1%
10 114
11.4%
ValueCountFrequency (%)
10 114
11.4%
9 111
11.1%
8 89
8.9%
7 99
9.9%
6 101
10.1%
5 82
8.2%
4 103
10.3%
3 89
8.9%
2 100
10.0%
1 112
11.2%

Maintenance Cost ($)
Real number (ℝ)

Distinct361
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.9
Minimum100
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:55.944846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile121
Q1188
median294
Q3392
95-th percentile476.05
Maximum499
Range399
Interquartile range (IQR)204

Descriptive statistics

Standard deviation114.65286
Coefficient of variation (CV)0.39010842
Kurtosis-1.2256181
Mean293.9
Median Absolute Deviation (MAD)102
Skewness0.037906088
Sum293900
Variance13145.279
MonotonicityNot monotonic
2025-01-26T18:13:56.123787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188 8
 
0.8%
356 8
 
0.8%
486 8
 
0.8%
158 7
 
0.7%
437 7
 
0.7%
179 7
 
0.7%
155 7
 
0.7%
137 6
 
0.6%
359 6
 
0.6%
424 6
 
0.6%
Other values (351) 930
93.0%
ValueCountFrequency (%)
100 3
0.3%
101 3
0.3%
102 1
 
0.1%
103 2
0.2%
104 3
0.3%
105 2
0.2%
106 3
0.3%
107 2
0.2%
108 3
0.3%
109 3
0.3%
ValueCountFrequency (%)
499 3
 
0.3%
498 1
 
0.1%
496 3
 
0.3%
495 1
 
0.1%
494 2
 
0.2%
492 2
 
0.2%
490 1
 
0.1%
489 1
 
0.1%
487 3
 
0.3%
486 8
0.8%

Type
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
509 
1
491 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Length

2025-01-26T18:13:56.277868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-26T18:13:56.386452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Most occurring characters

ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 509
50.9%
1 491
49.1%

Efficiency Score
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.218
Minimum48
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-01-26T18:13:56.511117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile59
Q170
median81
Q391
95-th percentile100
Maximum100
Range52
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.886448
Coefficient of variation (CV)0.16064285
Kurtosis-0.88693451
Mean80.218
Median Absolute Deviation (MAD)10
Skewness-0.2029268
Sum80218
Variance166.06054
MonotonicityNot monotonic
2025-01-26T18:13:56.673931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 77
 
7.7%
77 32
 
3.2%
85 31
 
3.1%
74 29
 
2.9%
84 27
 
2.7%
88 27
 
2.7%
94 26
 
2.6%
87 26
 
2.6%
92 25
 
2.5%
70 25
 
2.5%
Other values (42) 675
67.5%
ValueCountFrequency (%)
48 1
 
0.1%
49 3
 
0.3%
51 4
0.4%
52 4
0.4%
53 8
0.8%
54 2
 
0.2%
55 5
0.5%
56 5
0.5%
57 9
0.9%
58 6
0.6%
ValueCountFrequency (%)
100 77
7.7%
99 17
 
1.7%
98 10
 
1.0%
97 20
 
2.0%
96 24
 
2.4%
95 17
 
1.7%
94 26
 
2.6%
93 23
 
2.3%
92 25
 
2.5%
91 23
 
2.3%

Interactions

2025-01-26T18:13:51.889983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.109712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.044065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.928825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.925025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.791460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.902800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.012189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.260523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.166801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.063989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.045133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.914516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.039843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.135000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.393992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.293792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.204526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.165631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.029512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.175898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.282908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.534284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.420854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.346474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.299640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.406831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.332603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.406640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.667506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.540422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.477716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.412795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.521187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.477702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.525375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.789369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.658437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.653606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.527797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.642610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.612659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:52.658682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:46.915618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:47.799232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:48.797147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:49.661521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:50.774267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-26T18:13:51.752425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-26T18:13:56.782956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Airflow (m³/min)Build Quality RatingCooling Capacity (BTU)EEREfficiency ScoreMaintenance Cost ($)Noise Level (dB)Power Consumption (kW)Temperature Range (°C)TypeWarranty (Years)
Airflow (m³/min)1.0000.0250.000-0.046-0.0320.002-0.0310.0130.0320.0000.000
Build Quality Rating0.0251.0000.0440.0240.4470.027-0.002-0.0400.0610.0000.054
Cooling Capacity (BTU)0.0000.0441.0000.0000.0300.0290.0000.0580.0000.0000.028
EER-0.0460.0240.0001.0000.8570.040-0.0520.0550.0000.0610.044
Efficiency Score-0.0320.4470.0300.8571.000-0.123-0.0890.0300.0580.0870.000
Maintenance Cost ($)0.0020.0270.0290.040-0.1231.000-0.0140.0100.0000.0670.000
Noise Level (dB)-0.031-0.0020.000-0.052-0.089-0.0141.000-0.0010.0620.0000.032
Power Consumption (kW)0.013-0.0400.0580.0550.0300.010-0.0011.0000.0250.0070.043
Temperature Range (°C)0.0320.0610.0000.0000.0580.0000.0620.0251.0000.0160.016
Type0.0000.0000.0000.0610.0870.0670.0000.0070.0161.0000.002
Warranty (Years)0.0000.0540.0280.0440.0000.0000.0320.0430.0160.0021.000

Missing values

2025-01-26T18:13:52.827457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-26T18:13:53.034096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Power Consumption (kW)Cooling Capacity (BTU)Noise Level (dB)EERTemperature Range (°C)Airflow (m³/min)Warranty (Years)Build Quality RatingMaintenance Cost ($)TypeEfficiency Score
02.872400047.393.55209.20310188096
15.751800044.663.283012.0156297177
24.663000046.253.823029.2365381183
33.99900040.672.721817.4239299175
41.783000057.233.773021.9033198084
51.78900046.273.503023.9753126179
61.291200057.063.313016.0757188177
75.33900037.644.412011.24672450100
84.012400059.713.553012.5426281186
94.541800046.082.943014.8923356065
Power Consumption (kW)Cooling Capacity (BTU)Noise Level (dB)EERTemperature Range (°C)Airflow (m³/min)Warranty (Years)Build Quality RatingMaintenance Cost ($)TypeEfficiency Score
9905.002400048.672.532513.6134259161
9914.473000039.513.663024.89510183092
9922.36900046.462.713024.5461213155
9933.951200049.024.251820.9424322094
9942.801200056.603.552012.6844487075
9951.461200047.033.903010.132102651100
9965.592400057.443.32159.4169290182
9971.683000051.863.621821.0463274079
9985.752400038.424.392526.1562100094
9993.231800059.872.672526.8869486162